FaiceTech's platform includes layered guardrails: technical and governance controls designed to reduce misuse, improve accountability and support proportionate processing of sensitive data. These safeguards are designed to support responsible operation in real-world environments.
Each safeguard is part of the platform itself — not an optional add-on or an afterthought.
Sensitive actions can require structured justification reasons, helping organisations evidence why actions were taken. Examples may include actions such as adding or removing watchlist subjects, restoring records, deleting records or disabling biometric processing.
Watchlist management is designed to support accountable operation. Users can manage subject inclusion and removal, while structured reasons help evidence the purpose behind sensitive changes. The platform supports client-defined watchlists and subject relationships while maintaining accountability over why subjects are included.
Retention, deletion, restoration and archival behaviours are designed to be intentional and policy-driven rather than implicit. The platform is designed to calculate destruction dates and support controlled review or restoration workflows where justified.
Platform access is designed around role-based permissions, tenant isolation and scoped access so that users and services access only what they need. These controls help prevent unauthorised access and support separation between customer deployments.
The platform supports human review and escalation paths for sensitive actions and high-impact classifications. This helps ensure technology supports accountable human decision-making rather than replacing it.
Administrative, governance and operational actions are designed to be recorded with actor, timestamp, context and justification metadata. This supports traceability, investigations, operational assurance and compliance review.
Where images are attached to subjects, the platform can reduce unnecessary processing of unrelated individuals through redaction or blurring of detected faces other than the intended subject. This supports data minimisation and reduces collateral privacy impact.